Course Outline


Keras and Deep Learning Frameworks

  • TensorFlow and Theano back-ends
  • Keras vs Tensorflow

Data and Machine Learning

  • Tabular data, visual data, unstructured data, etc.
  • Unsupervised learning, supervised learning, reinforcement learning, etc.

Preparing the Development Environment

  • Installing and configuring Anaconda
  • Installing Keras with a TensorFlow back-end

Neural Networks in Keras

  • Using Keras functional API to build a network
  • Pre-processing and fitting data
  • Defining a Keras model

Mutiple Input and Output Networks

  • Building two input-networks
  • Representing high-cardinality data
  • Merging layers
  • Extending the two input-network
  • Building neural networks with multiple outputs
  • Solving multiple problems simultaneously

Training and Pre-Training

  • Training models
  • Saving and loading models
  • Using ResNet50 on models


  • Exporting Keras logs
  • Visualizing a computational graph and training progress

Google Cloud

  • Exporting models
  • Uploading Keras models
  • Using a model in Google Cloud

Summary and Conclusion


  • An understanding of basic linear algebra


  • Software Engineers
 14 Hours

Testimonials (4)

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